Image processing apparatus, medical imaging apparatus, and image processing method

ABSTRACT

Disclosed herein are an image processing apparatus, a medical imaging apparatus, and an image processing method, which may intuitively and easily set an image processing parameter used to process a medical image to a user-preferred optimal value. The image processing apparatus includes a display unit configured to display a plurality of sample images to which at least one image processing parameter has been variably applied; an input unit configured to receive a selection of one from among the displayed plurality of sample images from a user; and an image processing unit configured to generate a plurality of new sample images to which the at least one image processing parameter has been variably applied based on an image processing parameter to be applied to the selected sample image when the user is not satisfied with the selected sample image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Korean Patent Application No.10-2015-0008848, filed on Jan. 19, 2015 in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein byreference in its entirety.

BACKGROUND

1. Field

Exemplary embodiments relate to an image processing apparatus, a medicalimaging apparatus, and an image processing method, in which a user mayset a parameter which is applicable for processing a medical image.

2. Description of the Related Art

A medical imaging apparatus is an apparatus for imaging an inside of atarget object for the purpose of facilitating a diagnostic or surgicalprocedure. Various examples of the medical imaging apparatus include amedical ultrasound imaging apparatus, a typical radiography apparatus, amagnetic resonance imaging (MRI) apparatus, a mammography apparatus, apositron emission tomography (PET) apparatus, a computed tomography (CT)apparatus, a single photon emission computed tomography (SPECT)apparatus, an optical coherence tomography (OCT) apparatus, and thelike.

A medical image acquired by a medical imaging apparatus is processed andthen displayed on a display device. A user, such as a doctor and/or aradiologic technologist, may perform a diagnostic or surgical procedureor control photographing to obtain a medical image.

SUMMARY

Therefore, it is an aspect of one or more exemplary embodiments toprovide an image processing apparatus, a medical imaging apparatus, andan image processing method, which may intuitively and easily set animage processing parameter which is usable for processing a medicalimage to a user-preferred optimal value.

Additional aspects of the exemplary embodiments will be set forth inpart in the description which follows and, in part, will be apparentfrom the description, or may be learned by practice of the exemplaryembodiments.

In accordance with one aspect of one or more exemplary embodiments, animage processing apparatus includes a display configured to display afirst plurality of sample images to which at least one image processingparameter from among a plurality of image processing parameters has beenvariably applied; an input device configured to receive, from a user, aselection of one sample image from among the displayed first pluralityof sample images; and an image processor configured to generate a secondplurality of new sample images to which the at least one imageprocessing parameter has been variably applied based on an imageprocessing parameter applied to the selected sample image when the useris not satisfied with the selected sample image.

The image processing apparatus may further include a parametercontroller configured to control the plurality of image processingparameters applied by the image processor.

The parameter controller may be further configured to optimize the atleast one image processing parameter applied to the selected sampleimage based on machine learning.

The image processor may be further configured to repeatedly perform thegeneration of the second plurality of new sample images until the useris satisfied with the selected sample image, and the display may befurther configured to repeatedly perform a display of the generatedsecond plurality of sample images.

The image processing apparatus may further include a storage configuredto store preference data which relates to respective preferences of eachof a plurality of users for the plurality of image processing parametersin which the preference data includes a respective setting history ofthe plurality of image processing parameters for each of the pluralityof users.

The parameter controller may be further configured to set the at leastone image processing parameter based on a sample image finally selectedby the user.

In accordance with another aspect, an image processing apparatusincludes: a display configured to display a first plurality of sampleimages to which a first image processing parameter from among aplurality of image processing parameters has been variably applied to asecond plurality of sample images to which an nth image processingparameter from among the plurality of image processing parameters hasbeen variably applied, where n is an integer that is greater than orequal to two; an input device configured to receive, from a user, aselection of one sample image from among the displayed first pluralityof sample images; and a parameter controller configured to set the firstimage processing parameter based on the nth image processing parameterapplied to the selected sample image.

The parameter controller may be further configured to store theplurality of image processing parameters that are applied to theselected sample image.

The parameter controller may be further configured to optimize theplurality of image processing parameters that are applied to theselected sample image based on machine learning and to store theoptimized plurality of image processing parameters.

In accordance with still another aspect, an image processing apparatusincludes: a display configured to display a first plurality of sampleimages to which at least a first image processing parameter from among aplurality of image processing parameters has been variably applied; aninput device configured to receive, from a user, a selection of onesample image from among the displayed first plurality of sample images;and a parameter controller configured to set the at least first imageprocessing parameter based on a second image processing parameter fromamong the plurality of image processing parameters that is applied tothe selected sample image, and when the at least first image processingparameter is changed, to optimize the changed image processing parameterbased on machine learning.

The parameter controller may be further configured to perform a settingof the at least first image processing parameter upon an initialexecution performed by the image processing apparatus or periodically.

The parameter controller may be further configured to include thechanged image processing parameter in learning data which is used forthe application of the machine learning.

In accordance with even another aspect, a medical imaging apparatusincludes: a scanner configured to scan a target object in order toacquire a medical image; and an image processing apparatus configured toset at least a first image processing parameter from among a pluralityof image processing parameters to be applied to the medical image, inwhich the image processing apparatus includes a display configured todisplay a first plurality of sample images to which the at least firstimage processing parameter has been variably applied; an input deviceconfigured to receive, from a user, a selection of one sample image fromamong the displayed first plurality of sample images; and an imageprocessor configured to generate a second plurality of new sample imagesfor which the at least first image processing parameter has beenvariably applied based on an image processing parameter applied to theselected sample image when the user is not satisfied with the selectedsample image.

In accordance with yet another aspect, an image processing methodincludes displaying a first plurality of sample images to which at leasta first image processing parameter from among a plurality of imageprocessing parameters has been variably applied; receiving, from a user,a selection of one sample image from among the displayed first pluralityof sample images; and generating a second plurality of new sample imagesto which the at least first image processing parameter has been variablyapplied based on an image processing parameter applied to the selectedsample image when the user is not satisfied with the selected sampleimage.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of exemplary embodiments,taken in conjunction with the accompanying drawings of which:

FIG. 1 is a control block diagram illustrating an image processingapparatus, according to an exemplary embodiment;

FIG. 2 is a view illustrating apparatuses that may use, as an element,an image processing apparatus, according to an exemplary embodiment;

FIG. 3 is a control block diagram illustrating a medical imagingapparatus, according to an exemplary embodiment;

FIG. 4 is a control block diagram illustrating a case in which a medicalimaging apparatus acquires an X-ray image;

FIG. 5 is a diagram illustrating a configuration of an X-ray tube;

FIG. 6 is a diagram illustrating a configuration of an X-ray detector;

FIG. 7 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires a general radiography image;

FIG. 8 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires an X-ray image of a breast;

FIG. 9 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires a computed tomography image;

FIG. 10 is a control block diagram illustrating a case in which amedical imaging apparatus acquires a magnetic resonance image;

FIG. 11 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires a magnetic resonance image;

FIGS. 12 and 13 are diagrams illustrating a display unit on which ascreen for setting an image processing parameter is displayed;

FIG. 14 is an exemplary diagram schematically illustrating a process ofa user selecting one of a plurality of sample images;

FIGS. 15 and 16 are diagrams illustrating a method of providing a sampleimage;

FIGS. 17 and 18 are diagrams schematically illustrating a process of auser selecting one of a plurality of sample images;

FIGS. 19 and 20 are diagrams illustrating a process of changing an imageprocessing parameter that has previously been set;

FIG. 21 is a diagram illustrating an example of setting an imageprocessing parameter once in an initial step and then applying learningwhenever the parameter is changed by a user;

FIGS. 22 and 23 are diagrams illustrating another example of providing aplurality of sample images to a user;

FIG. 24 is a diagram illustrating an example of enlarging and displayinga portion of a sample image;

FIG. 25 is a diagram illustrating an example of setting an imageprocessing parameter for each human part;

FIG. 26 is a flowchart illustrating an image processing method,according to an exemplary embodiment;

FIG. 27 is a flowchart illustrating another example of an imageprocessing method, according to an exemplary embodiment; and

FIG. 28 is a flowchart illustrating still another example of an imageprocessing method, according to an exemplary embodiment.

DETAILED DESCRIPTION

Hereinafter, a medical imaging apparatus, an image processing apparatus,and an image processing method according to one or more exemplaryembodiments will be described in detail with reference to theaccompanying drawings.

FIG. 1 is a control block diagram illustrating an image processingapparatus, according to an exemplary embodiment.

Referring to FIG. 1, an image processing apparatus 100 according to anexemplary embodiment includes a parameter control unit (also referred toherein as a “parameter controller”) 110 configured to control at leastone image processing parameter to be applied with respect to a medicalimage, an image processing unit (also referred to herein as an “imageprocessor”) 120 configured to perform image processing with respect tothe medical image, a storage unit (also referred to herein as a “storagedevice” and/or as a “storage”) 130 configured to store a user'spreference for the image processing parameter, a display unit (alsoreferred to herein as a “display device” and/or as a “display”) 141configured to display a plurality of sample images for which imageprocessing parameters have been variably applied, and an input unit(also referred to herein as an “input device”) 142 configured to receivea selection of an image processing parameter from the user.

The image processing unit 120 may receive a medical image, and the imageprocessing unit 120 is configured to perform image processing operationswith respect to the received medical image. However, the medical imagereceived by the image processing unit 120 does not necessarily need tohave the form of an image that is recognizable by a user, but mayinclude a set of data that may become the recognizable image. Forexample, scan data which relates to a target object may be received bythe image processing unit 120 and processed by the image processing unit120, thus completely converting the scan data into the form of arecognizable image.

The image processing operations performed by the image processing unit120 may include pre-processing operations and post-processingoperations. The pre-processing operations may be performed by convertingmeasurement data acquired by scanning a target object into an intendedimage, and the post-processing operations may be performed for imageenhancement or performed to display an image in a user's desiredviewpoint. Detailed processes performed during the pre-processingoperations and the post-processing operations may vary depending on atype of the medical image that is subject to the image processing. Forexample, the pre-processing operations with respect to a computedtomography image may be intended to convert profile values ofmeasurement data obtained via projections into a desired form, and maybe performed by comparison with data stored in a calibration file. Thepost-processing operations may be intended to adjust a parameter such asany of contrast, brightness, noise, sharpness, a structure, latitude,and the like, and may be performed by adjusting the parameter to auser's desired value.

The parameter control unit 110 is configured to control a series ofprocesses for setting an image processing parameter as a user's desiredvalue. In particular, the image processing parameter may include aparameter to be applied for the post-processing operations, for example,any of a parameter for adjusting contrast, a parameter for adjustingbrightness, a parameter for adjusting noise, a parameter for adjustingsharpness, and the like. The parameter may be referred to as an imagelook parameter.

The parameter control unit 110 may generate a plurality of sample imagesby controlling the image processing unit 120 to perform different imageprocessing operations on the same image, display the generated pluralityof sample images via the display unit 141, and perform an operation ofsetting an image processing parameter or an operation of performing anext image processing operation according to a user's selection. Moredetailed operations of the parameter control unit 110 will be describedbelow.

The parameter control unit 110 and the image processing unit 120 mayinclude a memory having a program stored therein that can execute eachoperation and a processor configured to execute the program stored inthe memory. The parameter control unit 110 and the image processing unit120 may include respective processors and respective memories and mayshare a processor and memory. In particular, the memories may beincluded in the storage unit 130 to be described below, or may beprovided separately from the storage unit 130. In addition, a learningmodule and/or a determination module of the parameter control unit 110to be described below may be implemented by using separate processors ormay share the same processor.

The storage unit 130 may include a storage medium, for example, any of asemiconductor memory such as a random access memory (RAM), a read onlymemory (ROM), and a flash memory, a magnetic memory such as a magneticdisk, and an optical disc such as a CR-ROM.

The storage unit 130 may include a user preference database (DB) inwhich a user's preference for an image processing parameter is stored,and the image processing unit 120 may generate a plurality of sampleimages based on the stored user's preference. The user's preference forthe image processing parameter may include data that relates toparameters generally preferred by many users or a combination thereof,or statistical data that relates to the parameters. For example, theuser preference DB may stores values of image processing parameters thatare selected or set when many users display a medical image, orstatistical data that relates to the values.

The storage unit 130 may store preferences of many users, and many usersmay use the same or different image processing apparatuses 100.Furthermore, the users may include a radiologic technologist, a doctor,or the like who acquires a medical image or provides medical diagnosisor treatment using the medical image, irrespective of the use of theimage processing apparatus 100.

The display unit 141 displays the sample images generated by the imageprocessing unit 120 that performs image processing operations.Furthermore, the display unit 141 may display information that relatesto photographing conditions for the medical image, informationassociated with control of the image processing parameters, a screen forleading to a user's selection, and so on.

The display unit 141 may be implemented as at least one of variousdisplay devices, such as a liquid crystal display (LCD), a lightemission diode (LED), a plasma display panel (PDP), and an organic lightemission diode (OLED).

The input unit 142 may be implemented as at least one of various inputdevices, such as a jog shuttle, a trackball, a button, a mouse, akeyboard, and a touch panel. When the input unit 142 is implemented as atouch panel, the input unit 142 may be combined with the display unit141 to form a touch screen.

FIG. 2 is a view illustrating apparatuses that may use, as an element,the image processing apparatus, according to an exemplary embodiment.

Referring to FIG. 2, the image processing apparatus 100 according to anexemplary embodiment may be included in, or implemented as, a medicalimaging apparatus 200 that is configured to scan a target object inorder to acquire a medical image. Alternatively, the image processingapparatus 100 may be included in, or implemented as, a central server300 that integrally stores and manages medical images acquired by themedical imaging apparatus 200. In particular, the central server 300 maybe a picture archive communication system (PACS). Alternatively, thecentral server 300 may be included in, or implemented as, a usercomputer 400 that is provided separately from the medical imagingapparatus 200.

The medical imaging apparatus 200, the central server 300, and the usercomputer 400 may communicate with each other via a network. Accordingly,the medical image acquired by the medical imaging apparatus 200 may betransmitted to the central server 300 or the user computer 400. For thispurpose, the image processing apparatus 100 may include one or morecommunication modules which are configured for communicating with anexternal apparatus. For example, the image processing apparatus 100 mayinclude any of a short-range communication module, a wired communicationmodule, and a wireless communication module.

The short-range communication module denotes a module that is configuredfor performing short-range communication with an apparatus that ispositioned within a certain distance. Examples of a short-distancecommunication technology that may be applied to an embodiment include,but are not limited to, wireless LAN, Wi-Fi, Bluetooth, Zigbee, Wi-FiDirect (WFD), ultra wideband (UWB), Infrared Data Association (IrDA),Bluetooth Low Energy (BLE), and near field communication (NFC).

The wired communication module denotes a communication module that isconfigured for performing communication by using an electronic signal oran optical signal. The wired communication technology will include awired communication technology using any of a pair cable, a coaxialcable, and an optical fiber cable. However, the exemplary embodimentsare not limited thereto, and the wired communication technology mayinclude a wired communication technology that is known to one of skillin the art.

The wireless communication module may include an antenna or a wirelesscommunication chip that is configured for transmitting and/or receivinga wireless signal to and/or from at least one of a base station, anexternal apparatus, and a server in a mobile communication network. Forexample, the wireless communication module may support Wireless LANSpecification IEEE802.11x of Institute of Electrical and ElectronicsEngineers (IEEE).

As described above, the image processing apparatus 100 may be includedin, or implemented as, at least one of the medical imaging apparatus200, the central server 300, and the user computer 400. However, forconvenience of description, it will be described below that the imageprocessing apparatus 100 is included in the medical imaging apparatus200.

FIG. 3 is a control block diagram illustrating a medical imagingapparatus, according to an exemplary embodiment.

The medical imaging apparatus 200 includes a scanner 210 configured toscan a target object to acquire a medical image, a scan control unit(also referred to herein as a “scan controller”) 220 configured tocontrol a scan parameter, and the image processing apparatus 100.

The scanner 210 may deliver the acquired medical image to the imageprocessing apparatus 100 and may have a variable configuration andoperation based on the type of the medical image. In addition, the typeof a scan parameter the scan control unit 220 is configured to controlmay vary based on the type of the medical image. The configuration ofthe scanner 210 and the operation of the scan control unit 220 aredescribed below according to the type of the medical image.

FIG. 4 is a control block diagram illustrating a case in which a medicalimaging apparatus acquires an X-ray image, FIG. 5 is a diagramillustrating a configuration of an X-ray tube, and FIG. 6 is a diagramillustrating a configuration of an X-ray detector.

Referring to FIG. 4, when the medical imaging apparatus 200 acquires theX-ray image, the scanner 210 may include an X-ray tube 211 configured toproduce and irradiate X-rays and an X-ray detector 212 configured todetect the irradiated X-rays.

Referring to FIG. 5, the X-ray tube 211 may be implemented as a diodevacuum tube which includes an anode 211 c and a cathode 211 e. Thecathode 211 e includes a filament 211 h and a focusing electrode 211 gthat is configured to focus electrons, and the focusing electrode 211 gis referred to an a focusing cup.

The inside of a glass tube 211 k is evacuated to a high vacuum state ofabout 10 mm Hg, and the filament 211 h of the cathode 211 e may beheated to a high temperature in order to generate thermal electrons. Forexample, the filament 211 h may be a tungsten filament, and the filament211 h may be heated by applying a current to electrical leads 211 fconnected to the filament 211 h.

The anode 211 c may be made of copper, and a target material 211 d isapplied or disposed on a surface of the anode 111 c facing the cathode211 e. The target material 211 d may be a high-resistance material, suchas, e.g., any of Cr, Fe, Co, Ni, W, and/or Mo. The target material issloped at a certain angle. As the sloped angle increases, a focal spotsize decreases. In addition, the focal spot size may vary based on atube voltage, a tube current, a size of the filament, a size of thefocusing electrode, and/or a distance between the anode and the cathode.

When a high voltage is applied between the cathode 211 e and the anode211 c, thermal electrons are accelerated and collide with the targetmaterial 211 d of the anode 211 c, thereby generating X-rays. Thegenerated X-rays are emitted to the outside via a window 211 i. Thewindow 211 i may be formed of a beryllium (Be) thin film. Also, thoughnot shown in FIG. 5, a filter may be provided on the front or rear sideof the window 211 i in order to filter a specific energy band of X-rays.In addition, a collimator may be disposed on the front side of thewindow 211 i in order to adjust a field of view (FOV) of the X-rays andreduce scattering of X-rays.

The target material 211 d may be rotated by a rotor 211 b. When thetarget material 211 d rotates, a heat accumulation rate may increase byten times per unit area and the focal spot size may be reduced, ascompared to when the target material 211 d is fixed.

The voltage that is applied between the cathode 211 e and the anode 211c of the X-ray tube 211 is called a tube voltage. The magnitude of thetube voltage may be expressed as a crest value (kVp). When the tubevoltage increases, a velocity of thermal electrons increasesaccordingly. Then, an energy (energy of photons) of X-rays that aregenerated when the thermal electrons collide with the target material211 d also increases.

The current flowing through the X-ray tube 211 is called a tube current,and can be expressed as an average value (mA). When the tube currentincreases, the number of thermal electrons emitted from the filament 211h increases, and as a result, a dose of X-rays (that is, the number ofX-ray photons) that are generated when the thermal electrons collidewith the target material 211 d increases correspondingly.

In summary, an energy level of X-rays can be controlled by adjusting atube voltage. Also, a dose or intensities of X-rays can be controlled bymultiplication (mAs) of a tube current (mA) and an X-ray exposure time(s).

When the X-rays to be irradiated have a predetermined energy band, thepredetermined energy band may be defined by upper and lower limits. Thedifferent energy bands may have at least one of upper and lower limitsof energy bands which are different from one another.

The upper limit of the predetermined energy band, that is, maximumenergy of X-rays to be irradiated, may be adjusted based on themagnitude of a tube voltage, and the lower limit of the predeterminedenergy band, that is, minimum energy of X-rays to be irradiated, may beadjusted by using a filter. By filtering out a low energy band of X-raysusing the filter, an average energy level of X-rays to be irradiated mayincrease.

When X-rays irradiated by the X-ray tube 211 are incident to the X-raydetector 212 after propagating through a target object, the X-raydetector 212 detects and converts the incident X-rays into electricalsignals. The electrical signals correspond to X-ray image signals.

The X-ray detector 212 can be classified according to its materialconfiguration, a method of converting detected X-rays into electricalsignals, and/or a method of acquiring electrical signals.

The X-ray detector 212 may be divided into a mono type device or ahybrid type device according to its material configuration.

If the X-ray detector 212 is a mono type device, a part configured fordetecting X-rays and generating electrical signals and a part configuredfor reading and processing the electrical signals may be made of thesame semiconductor material, or may be manufactured by one process. Inthis case, the X-ray detector 212 may be a charge-coupled device (CCD)or a complementary metal-oxide semiconductor (CMOS) which is a lightreceiving device.

If the X-ray detector 212 is a hybrid type device, a part configured fordetecting X-rays and generating electrical signals and a part configuredfor reading and processing the electrical signals may be made ofdifferent materials, or may be manufactured by different processes. Forexample, there are a case of detecting X-rays by using a photodiode, aCCD, or a light receiving device such as CdZnTe, and reading andprocessing electrical signals by using a CMOS readout integrated circuit(CMOS ROIC), a case of detecting X-rays by using a strip detector, andreading and processing electrical signals by using a CMOS ROIC, and acase of using an amorphous silicon (a-Si) or an amorphous selenium(a-Se) flat panel system.

The X-ray detector 212 may use a direct conversion mode and/or anindirect conversion mode according to a method of converting X-rays intoelectrical signals.

In the direct conversion mode, if X-rays are irradiated, electron-holepairs are temporarily generated in a light receiving device, electronsmove to an anode and holes move to a cathode due to an electric fieldwhich is applied to both terminals of the light receiving device, andthe X-ray detector 212 converts the movement of the electrons and holesinto an electrical signal. The light receiving device may be made of anyof a-Si, CdZnTe, HgI2, or PbI2.

In the indirect conversion mode, if X-rays irradiated from the X-raytube 211 react with a scintillator to emit photons having a wavelengthwithin a visible light region, the light receiving device detects andconverts the photons into an electrical signal. The light receivingdevice may be made of amorphous silicon (a-Si), and the scintillator mayinclude any of a gadolinium oxysulfide (GADOX) scintillator of a thinfilm type, and/or a thallium-doped cesium iodide (CsI (Tl)) of a micropillar type or a needle type.

The X-ray detector 212 may use a charge integration mode which entailsstoring charges during a predetermined time period and then acquiring asignal from the stored charges, or a photon counting mode which entailscounting the number of photons whenever a signal is generated by singleX-ray photons, according to a method of acquiring electrical signals.

As an example, the X-ray detector 212 may have a two-dimensional (2D)array structure having a plurality of pixels Px, as shown in FIG. 6.Referring to FIG. 6, the X-ray detector 212 may include a lightreceiving device (also referred to herein as a “light receiver”) 212 aconfigured to detect X-rays and to generate electrical signals, and aread-out circuit 212 b configured to read out the generated electricalsignals.

The light receiving device 212 a may be made of a single crystalsemiconductor material in order to ensure high resolution, high responsespeed, and a high dynamic area even under conditions of relatively lowenergy and a relatively small dose of X-rays. The single crystalsemiconductor material may include any of Ge, CdTe, CdZnTe, and/or GaAs.

The light receiving device 212 a may be in the form of a PIN photodiode.The PIN photodiode is fabricated by bonding a p-type semiconductorsubstrate 212 a-3 of a 2D array structure under an n-type semiconductorsubstrate 212 a-1 with high resistance.

The read-out circuit 212 b, which is fabricated according to a CMOSprocess, is in the form of a 2D array structure and may be coupled withthe p-type semiconductor substrate 212 a-3 of the light receiving device212 a in units of pixels. A flip-chip bonding (FCB) method of formingbumps 212 b by using any of solder (PbSn), indium (In), or the like,reflowing, applying heat, and then compressing may be used.

Meanwhile, the scanner 210 may include both of the X-ray tube 211 andthe X-ray detector 212 in a fabrication step. Alternatively, the scanner210 may include only the X-ray tube 211 in a fabrication step, and aseparate X-ray detector 212 may be used together with the scanner 210.In the latter case, the X-ray detector may be implemented in a portabledevice, and fabricated by the same manufacturer as or a differentmanufacturer from that of the medical imaging apparatus.

The scan control unit 220 may be configured to control scan parametersrelated to the X-ray tube 211, for example, any of a tube voltage, atube current, an exposure time, a filter type, and a type and a rotationspeed of a target material. Accordingly, the scan control unit 220 mayfacilitate a manual control of the scan parameters according to a user'sselection, or may perform auto exposure control that automaticallycontrols the scan parameters by using a scout image or a pre-shot image.In addition, the scan control unit 220 may control a signal read-out ofthe X-ray detector 212.

FIG. 7 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires a general radiography image, FIG. 8 is anappearance diagram illustrating a case in which a medical imagingapparatus acquires an X-ray image of a breast, and FIG. 9 is anappearance diagram illustrating a case in which a medical imagingapparatus acquires a computed tomography image.

Even if the inside of the target object is imaged using the same X-rays,different types of images may be generated according to a diagnosis thatis based on the X-rays or the purpose of a surgery operation. Forexample, the medical imaging apparatuses 200 that may acquire images byusing general radiography for photographing a still image of a chest, anarm, a leg, and the like, fluoroscopy for photographing a X-ray videosuch as image angiography, computed tomography for photographing atomography image or 3D image of a patient, mammography for photographingan X-ray image of a breast, and tomosynthesis for photographing atomography image or 3D image of a breast have different structures andoperations.

As an example, as shown in FIG. 7, when the medical imaging apparatus200 acquires a general radiography image, the X-ray tube 211 may bebuilt in a tube head 211 a connected to a ceiling of a radiation room,and a height of the tube head 211 a may be controlled. In addition, whenthe tube head 211 a is implemented in a sealing type, the tube head 211a may move forward, backward, left, and right along a guide railprovided on the ceiling of the radiation room.

A patient P is positioned between the X-ray tube 211 and the X-raydetector 212, and a part to be photographed may include any of a chest,an arm, a leg, etc.

In an example illustrated in FIG. 7, the X-ray detector 212 may beimplemented in a stand type. However, the medical imaging apparatus 200according to an exemplary embodiment is not limited thereto, and theX-ray detector 212 may be inserted into a patient table or implementedas a portable device.

The medical imaging apparatus 200 may include a workstation thatprovides a user interface, and any of various kinds of memories,microprocessors, and so on built in the workstation may controlprocessing or photographing of images. As an example, the imageprocessing apparatus 100 may be included in the work station, or theimage processing apparatus 100 may be implemented as the workstation, asshown in FIG. 7. In this case, a display unit 141 and an input unit 142may be provided to the workstation. However, this is merely an example,and all elements of the image processing apparatus 100 may notnecessarily be included in the workstation. Accordingly, some of theelements of the image processing apparatus 100 are included in theworkstation, and others may be included in the tube head 211 a.

As another example, as shown in FIG. 8, when the medical imagingapparatus 200 performs mammography to acquire a breast image, a breastof the patient P is positioned between the tube head 211 a and the X-raydetector 212, and a pressure paddle 207 is further included between thetube head 211 a and the X-ray detector 212 in order to compress thebreast. When the pressure paddle 207 compresses the breast, thethickness thereof decreases in a direction in which X-rays areirradiated, thereby helping implement a low dose and enabling materialswhich are vertically overlapping with respect to each other to be spreadlaterally.

Even when the medical imaging apparatus 200 performs mammography, themedical imaging apparatus 200 may include a workstation. As describedabove, the image processing apparatus 100 may be included in aworkstation, or as shown in FIG. 8, the image processing apparatus 100may be implemented as the workstation.

As still another example, as shown in FIG. 9, when the medical imagingapparatus 200 performs computed tomography to acquire a tomographyimage, the X-ray tube 211 and the X-ray detector 212 are positioned toface each other and are installed in a gantry 202. When a patient table201 transports the patient P to a bore 204, the gantry 202 scans thepatient P while rotating around the bore 204, and thus acquiresprojection data.

In this case, projection data that relates to the patient P is acquiredby the scanner 210, and the projection data is input to the imageprocessing apparatus 100. Likewise, the image processing apparatus 100may be included in the workstation, or implemented as the workstation asshown in FIG. 9, and the display unit 141 and the input unit 142 may beprovided to the workstation.

The image processing unit 120 may reconstruct the projection data inorder to generate a sectional plane image, or accumulate a plurality ofsectional plane images in order to generate a three-dimensional (3D)volume data.

FIG. 10 is a control block diagram illustrating a case in which amedical imaging apparatus acquires a magnetic resonance image, and FIG.11 is an appearance diagram illustrating a case in which a medicalimaging apparatus acquires a magnetic resonance image.

Referring to FIG. 10, when the medical imaging apparatus 200 acquires amagnetic resonance image, the scanner 210 may include a static magneticfield coil 213 a configured to form a static magnetic field, a gradientcoil 213 b configured to apply a gradient to the static magnetic fieldin order to form a gradient magnetic field, and a radio frequency (RF)coil 213 c configured to apply an RF pulse to a target object in orderto excite an atomic nucleus and receive an echo signal from the atomicnucleus, and the scan control unit 220 may control movement of thepatient table 201 or control an intensity and a direction of the staticmagnetic field, design a pulse sequence appropriate for a diagnosis partor a diagnosis purpose of a patient, and control operations of thescanner 210 according to the pulse sequence.

Referring to FIG. 11, the static magnetic field coil 213 a, the gradientcoil 213 b, and the RF coil 213 c are included in a magnetic assembly213 that surrounds the bore 204.

When the patient table 201 is transported to the bore 204 in which thestatic magnetic field is formed, the gradient magnetic field and the RFpulse are applied to excite an atomic nucleus included in the patient P,and then an echo signal is received from the atomic nucleus, and theecho signal is used to image the inside of the target object.

Likewise, the image processing apparatus 100 may be included in theworkstation, or implemented as the workstation, as shown in FIG. 10. Inthis case, the display unit 141 and the input unit 142 may be providedto the workstation.

The image processing unit 120 may receive an echo signal and mayreconstruct the echo signal in order to generate a sectional planeimage, or accumulate a plurality of sectional plane images in order togenerate a three-dimensional (3D) volume data.

FIGS. 12 and 13 are diagrams illustrating a display unit on which ascreen for setting an image processing parameter is displayed.

As shown in FIG. 12, a button 10 configured for setting the imageprocessing parameter may be displayed on the display unit 141. When thebutton 10 is selected, an image processing parameter setting menu isexecuted. The execution of the image processing parameter setting menumay include a series of processes which enable a user to select theimage processing parameter.

The button 10 may be selected by the user by adjusting the input unit142. For example, when the input unit 142 includes a mouse or trackball,the user may select the image processing parameter setting menu bymanipulating the mouse or trackball to move a pointing tool, such as acursor displayed on a screen, and clicking the mouse or trackball whenthe pointing tool is positioned on the button 10. Alternatively, whenthe input unit 142 may be implemented as a touch panel, the user mayselect the image processing parameter setting menu by touching aposition corresponding to the button on the touch panel.

A window 11 that includes a list of scan parameters applied or to beapplied to scan of a patient may be displayed on the display unit 141,and a final medical image on which the image processing has beenperformed may be displayed in a medical image window 12. However, such ascreen configuration is merely an example. As long as a button forexecuting the image processing parameter setting menu is displayed, theremaining aspects of the screen configuration may be changed.

A screen as shown in FIG. 12 may be displayed whenever the imageprocessing apparatus 100 is booted, upon initial installation, byperiods, or whenever a new medical image is acquired. Alternatively,when the image processing apparatus 100 is in an on state, a button forexecuting an image processing parameter setting menu may be alwaysdisplayed on one side of the display unit 141. Alternatively, a buttonfor executing an entire setting menu of the image processing apparatus100 may be displayed, and settable items including an image processingparameter may be displayed when the button is selected. In particular,when the image processing parameter setting menu is selected, aconfiguration of a screen displayed on the display unit 141 is notspecially limited.

When the button 10 is selected in order to execute an image processingparameter setting menu, as shown in FIG. 13, a pop-up window 13 whichincludes a plurality of sample images 13 a, 13 b, 13 c, and 13 d isdisplayed. In an example of FIG. 13, four breast images are used as theplurality of sample images. However, the types or number of sampleimages is not limited. The types of the sample images may be determinedaccording to or irrespective of the type of medical image processed bythe image processing apparatus 100. In addition, the number of sampleimages may be set or changed by a user.

The plurality of sample images 13 a, 13 b, 13 c, and 13 d are obtainedby performing different respective image processing operations.Specifically, when the image processing parameter setting menu isexecuted, the image processing unit 120 performs image processingoperations in order to generate a plurality of sample images. In thiscase, different image processing operations are performed with respectto the respective sample images.

The parameter control unit 110 may control an image processing parameterapplied for the image processing unit 120 in order to generate thesample image. As described above, data that relates to a preference forthe image processing parameter may be stored in the storage unit 130.For example, a plurality of sample images that are generated first afterthe image processing parameter setting menu is set may be processedaccording to statistical data stored in the storage unit 130. Inparticular, the image processing may be performed according to aparameter combination generally preferred by many users.

The four sample images 13 a, 13 b, 13 c, and 13 d may have all or someof image processing parameters that are set to values which aredifferent from those of the other sample images. Whether the imageprocessing parameters applied to the respective sample images aredifferent is not limited. However, a combination of the image processingparameters applied to one sample image may vary.

As will be described below, when a user selects parameters to finallyconverge into the most preferred parameter combination, the user may usea learning result which relates to data stored in the storage unit 130.For this purpose, the parameter control unit 110 may include a learningmodule configured to perform machine learning with respect to the datastored in the storage unit 130 and a determination module configured todetermine an optimized image processing parameter based on a learningresult obtained by the learning module.

The machine learning is one field of artificial intelligence and denotesa process of enabling a computer to extract useful knowledge fromaccumulated data and draw a determination based on the extracted usefulknowledge. The machine learning has a generalization capacity, whichdenotes processing with respect to new data that is input via arepresentation that evaluates data. There are various algorithmsaccording to an approach of the machine learning, and as an example, anartificial neural network may be used.

The artificial neural network is obtained by modeling a structure of ahuman brain in which efficient recognition actions occur and areimplemented as hardware, software, or a combination thereof. A humanbrain is composed of neurons, which are a basic unit of a nerve cell andwhich are connected via synapses to process information non-linearly andin parallel. A human brain performs learning while adjusting aconnection form or connection intensity of synapses. In this aspect, thebrain adjust the connection intensity of the synapses by weakening aconnection between neurons that lead to a wrong answer and strengtheninga connection between neurons that lead to a correct answer.

As an example, a determination module that is learned by the learningmodule may include a deep neural network having a multilayer structure.The deep neural network is an example of the artificial neural network.The deep neural network has one or more hidden layers between an inputlayer and an output layer. Each layer may be formed of a plurality ofnodes that correspond to artificial neurons, and a connection relationbetween nodes in different layers may be determined by learning. Forexample, only nodes included in adjacent layers may be connectedaccording to a structure of a restricted Boltzmann machine (RBM). Assuch, learning performed by the learning module applying the deep neuralnetwork may be referred to as deep learning. When the learning modulegenerates a determination module by using the deep learning, thedetermination module may have a structure of the above-describedartificial neural network.

The learning module of the parameter control unit 110 may learn the datastored in the storage unit 130 according to the above-described methods.However, this is merely an example, and the data may be learned byapplying any of various machine learning methods other than theabove-described method.

FIG. 14 is an exemplary diagram schematically illustrating a process ofa user selecting one of a plurality of sample images.

In an example of FIG. 14, it is assumed that a plurality of sampleimages are referred to as sample image A, sample image B, sample imageC, and sample image D according to a combination of the image processingparameters. In a screen as shown in FIG. 13, in a condition that a userselects the sample image B (i.e., step 1 as illustrated on the left sideof FIG. 14), when the user is satisfied with the selected sample imageB, the user ends the image processing parameter setting menu and setsimage processing parameters according to the selected sample image B(i.e., lower option of step 2 as illustrated at center portion of FIG.14). For this purpose, when the user selects one of the sample images, apop-up window may be displayed on the display unit 141 to inquire aboutwhether to set parameters. In this aspect, the setting of the imageprocessing parameters denotes storing values of the image processingparameters that are to be applied when a medical image is subsequentlyprocessed.

When the user is not satisfied with the sample image B, the display unit141 may display new sample images (i.e., upper option of step 2 asillustrated at center portion of FIG. 14). To this end, the imageprocessing unit 120 may perform new image processing operation(s) withrespect to the same image. In this case, the image processing parameterapplied to the new image processing operation(s) may be determined basedon learning of the parameter control unit 110 and the sample imageselected in a previous step (step 1). For example, when the imageprocessing parameters applied to the sample image selected in theprevious step are input to the determination module formed by thelearning of the parameter control unit 110, a combination of the imageprocessing parameters determined in consideration of a current user'spreference may be output. In particular, the determination module mayoptimize the image processing parameter. In this exemplary embodiment, aprocess of the determination module determining the image processingparameters may be defined as optimization of the image processingparameters, and the image processing parameters determined by thedetermination module may be defined as image processing parametersoptimized in consideration of a user's preference.

The image processing unit 120 may perform image processing operation(s)according to the output combination of the image processing parameters.As an example, if the sample image B selected in the previous step is animage having high contrast, the image processing unit 120 may minimize avariation in the contrast to keep the high contrast, and change otherimage processing parameters to generate sample image B₁, sample imageB₂, and sample image B₃. When the plurality of sample images aredisplayed in step 2, the sample image B selected by the user in step 1may also by displayed.

When the user selects the sample image B₁ in step 2 and is satisfiedwith the sample image B₁, the user ends the image processing parametersetting menu and sets the parameter according to the sample image B₁(i.e., lower option of step 3 as illustrated on the right side of FIG.14).

However, when the user is still not satisfied with the sample image B₁,the display unit 141 may display additional new sample images (i.e.,upper option of step 3 as illustrated on the right side of FIG. 14). Tothis end, the image processing unit 120 may perform new image processingoperation(s) on the same image. In this case, the image processingparameter applied to the new image processing operation(s) may bedetermined based on learning of the parameter control unit 110 and thesample image B₁ selected in a previous step (step 2). The details arethe same as described above.

As an example, sample image B₁₁, sample image B₁₂, sample image B₁₃ maybe generated and displayed in step 3 by minimizing a variation in aspecific image processing parameter applied to the sample image B₁selected in the previous step (step 2) and changing the other imageprocessing parameters.

Likewise, when the user selects one of the displayed sample images, theuser may go to the next step or set the image processing parametersdepending on whether the user is satisfied with the selected sampleimage. Accordingly, it is possible to gradually converge into an imageprocessing parameter combination based on a current user's preference byshowing sample images via several steps and prompting the user to selecta desired image in each step and applying a selection of the user to alearning result to generate sample images in a next step.

When the user is satisfied with the selected sample image and ends theimage processing parameter setting menu, the parameter control unit 110may set an image processing parameter based on a finally selected sampleimage. For example, the parameter control unit 110 may input the imageprocessing parameter combination applied to the finally selected sampleimage to the determination module, output an optimal image processingparameter combination, which is preferred by the user, and set imageprocessing parameters according to the output image processing parametercombination.

As another example, the parameter control unit 110 may set imageprocessing parameters by using values of the image processing parametersapplied to the finally selected sample image without applying thelearning.

FIGS. 15 and 16 are diagrams illustrating a method of providing a sampleimage.

As shown in FIG. 15, an image used for the image processing unit 120 toperform image processing to generate sample images may be a medicalimage I_(a) acquired by the scanner 210. This image is a main image towhich a set image processing parameter is to be applied.

In step 1, sample image processing 1 is performed on the medical imageI_(a) in order to generate and display a plurality of sample imagesI_(a1-1), I_(a1-2), I_(a1-3), and I_(a1-4). When the image processingparameter setting menu is not ended, sample image processing 2 isperformed on the sample image selected in step 1 in order to generateand display a plurality of sample images I_(a2-1), I_(a2-2), I_(a2-3),and I_(a2-4). The step may be repeated until the user is satisfied withthe selected sample image. As such, when the sample images are generatedby using a main image to which the image processing is actually to beapplied, the user may select the image processing parameters moreintuitively.

Alternatively, as shown in FIG. 16, image processing may be performed onan image I_(s) stored in a sample image database (DB) in order togenerate a sample image. The sample image DB may be stored in thestorage unit 130, stored in a memory provided in the medical imagingapparatus 200 separately from the storage unit 130, or stored in anotherexternal server. An image to be used for sample image processing may beselected from the sample image DB arbitrarily, selected by a user,and/or selected automatically according to the type of the main image onwhich the image processing apparatus 100 will perform image processing.For example, when the image processing apparatus 100 performs imageprocessing with respect to a magnetic resonance image of a brain, theimage processing apparatus 100 may select the magnetic resonance imageof the brain from among the images stored in the sample image DB.

In step 1, sample image processing 1 is performed on the medical imageI_(s) in order to generate and display a plurality of sample imagesI_(s1-1), I_(s1-2), I_(s1-3), and I_(s1-4). When the image processingparameter setting menu is not ended, sample image processing 2 isperformed on the sample image selected in step 1 in order to generateand display a plurality of sample images I_(s2-1), I_(s2-2), I_(s2-3),and I_(s2-4). The step may be repeated until the user is satisfied withthe selected sample image. As such, when the image stored in the sampleimage DB is used, the sample image may be generated even when the mainimage is not yet acquired, such as upon initial execution.

FIGS. 17 and 18 are diagrams schematically illustrating a process of auser selecting one of a plurality of sample images.

In the above-described example of FIG. 14, the display unit 141 maydisplay sample images on which different image processing operationshave been performed via several steps and prompt a user to select one ofthe sample images, thereby gradually converging into an optimal imageprocessing parameter combination that is preferred by the user. However,as shown in FIGS. 17 and 18, a specific image processing parameter maybe selected in each step. This will be described below in detail.

In an example of FIGS. 17 and 18, it is assumed that noise, contrast,and sharpness, which are representative image processing parameters, areselected. First, referring to FIG. 17, a plurality of sample imagesdisplayed on the pop-up window 13 of the display unit 141 may includesample image N₁, sample image N₂, sample image N₃, and sample image N₄to which a noise parameter is variably applied. Other than noise, thesame parameter values are applied to the images. Alternatively, a valueoptimized with respect to the noise parameter value, that is, a valuedetermined according to the noise parameter value, is applied to theimages. In this step, when the user selects the sample image N₂, thenoise parameter is set according to the selected sample image N₂. Inthis case, the noise parameter may also reflect a user's preference.

In the next step, sample image S₁, sample image S₂, sample image S₃, andsample image S₄ to which a sharpness parameter has been applied variablymay be displayed. Other than sharpness, the same parameter values areapplied to the images. Alternatively, a value determined according tothe sharpness parameter value may be applied to the images. In thisstep, when the user selects the sample image S₃, the sharpness parameteris set according to the selected sample image S₃. In this case, thesharpness parameter may also reflect a user's preference.

In the next step, sample image C₁, sample image C₂, sample image C₃, andsample image C₄ to which a contrast parameter has been applied variablymay be displayed. Other than contrast, the same parameter values areapplied to the images. Alternatively, a value determined according tothe contrast parameter value may be applied to the images. In this step,when the user selects the sample image C₄, the contrast parameter is setaccording to the selected sample image C₄. In this case, the contrastparameter may also reflect multiple users' preferences stored in thestorage unit 130.

When the display and selection of the sample images for each parameterare completed, a final sample image that is based upon all of the user'sselections may be displayed to receive a confirmation as to whether theuser is satisfied with the image. If the noise parameter value appliedto the sample image N₂ is N₂, the sharpness parameter value applied tothe sample image S₃ is S₃, and the contrast parameter value applied tothe sample image C₄ is C₄, all of N₂, S₃, and C₄ may be applied to afinal sample image. When the user is satisfied with the final sampleimage, N₂, S₃, and C₄ are set to respective parameter values. When theuser is not satisfied with the final sample image, an image processingprocess, a sample image display process, and a user selection processare repeatedly performed.

Alternatively, the learning of the parameter control unit 110 may beapplied instead of setting the image processing parameters applied tothe selected sample image without change as described above. In thiscase, the image processing parameter values N₂, S₃, and C₄ may beinputted to the determination module, and when image processingparameter values determined by the determination module are output, afinal sample image on which image processing has been performed byapplying the image processing parameter values may be displayed.Alternatively, N₂, S₃, and C₄ are applied to the final sample imagewithout change, and when the user selects the final sample image, valuesoptimized by applying the learning may be set to the parameters.

In the above-described example of FIG. 17, a selection of the user inthe previous step is not reflected in the sample image displayed in thecurrent step. However, as shown in FIG. 18, the selection of the user inthe previous step may be reflected in the sample image displayed in thecurrent step.

In particular, when the user selects the sample image N₂, a noiseparameter of N₂ is applied to a sample image generated in the next stepin order to generate sample image S₁ _(_) _(N2), sample image S₂ _(_)_(N2), sample image S₃ _(_) _(N2), and sample image S₄ _(_) _(N2). Inaddition, when the user selects the S₃ _(_) _(N2) in the next step, asharpness parameter of S₃ is applied to the sample image generated inthe next step in order to generate sample image C₁ _(_) _(S3N2), sampleimage C₂ _(_) _(S3N2), sample image C₃ _(_) _(S3N2), and sample image C₄_(_) _(S3N2). In this case, although the final sample image is generatedand displayed separately, a sample image selected in the current stepmay be the final sample image. For example, as shown in FIG. 18, whenthe user selects the sample image C₄ _(_) _(S3N2), the user may use thesample image C₄ _(_) _(S3N2) to determine whether to execute an additionstep because all parameters selected by the user are reflected in thesample image C₄ _(_) _(S3N2).

Likewise, in an example of FIG. 18, the learning of the parametercontrol unit 110 may also be applied. In this case, the image processingparameter values N₂, S₃, and C₄ may be inputted to the determinationmodule formed via the learning, and when image processing parametervalues which are determined, i.e., optimized, by the determinationmodule are output, a final sample image to which the optimized imageprocessing parameter values have been applied may be displayed.Alternatively, N₂, S₃, and C₄ may be applied to the final sample imagewithout change, and when the user selects the final sample image, theoptimized values may be set to the parameters.

After setting the image processing parameter(s) according to theabove-described method, the user may change the image processingparameter(s). For example, the user may set image processing parametersusing the image I_(s) stored in the sample image DB when the imageprocessing apparatus 100 is powered on, but later the user may desire tochange or reset the image processing parameters because the user may notbe satisfied with a result of performing image processing with respectto a medical image received from the scanner 210. An exemplaryembodiment which relates to changing or resetting the parameters will bedescribed below.

FIGS. 19 and 20 are diagrams illustrating a process of changing an imageprocessing parameter that has previously been set. In an example ofFIGS. 19 and 20, it is assumed that the parameter is set by graduallyconverging into an image processing parameter combination that isdesired by the user as indicated by selections made in several steps, asshown in the example of FIG. 14.

In addition, in the example of FIGS. 19 and 20, it is assumed that thesample image the user finally selects is sample image B₁, and imageprocessing parameters are set based on an image processing parametercombination applied to the sample image B₁ and learning of the parametercontrol unit 110. When a final medical image on which image processingis performed by applying the set parameters is displayed on the medicalimage window 12, but the user is not satisfied with the parameters anddesires to change the parameters, the user may reselect the button 10for executing the image processing parameter setting menu that isdisplayed on the display unit 141 in order to request a change of theparameters. Alternatively, a button for changing parameters may befurther displayed independently of setting of the parameters.

When the button 10 is selected, the pop-up window 13 which includes aplurality of sample images is displayed on the display unit 141. Asshown in FIG. 19, this step may be connected with setting of the imageprocessing parameters that had been performed most recently. Inparticular, when the user selected the sample image B₁ in the setting ofthe image processing parameter that had been performed most recently, astep that is executed to change parameters is a next step of a case inwhich the user selects, but is not satisfied with, the sample image B₁.That is, upon setting image processing parameters, the user optimizesthe image processing parameters applied to the finally selected sampleimage again by applying the image processing parameters to thedetermination module. Accordingly, the image processing unit 120 mayperform image processing in order to generate sample image B₁₁, sampleimage B₁₂, and sample image B₁₃, and the display unit 141 may displaythe sample image B₁₁, sample image B₁₂, and sample image B₁₃ in additionto the sample image B₁.

When the user selects, but is not satisfied with, the sample image B₁₁,the image processing unit 120 may perform image processing in order togenerate sample image B₁₁₁, sample image B₁₁₂, and sample image B₁₁₃,and the display unit 141 may display the sample image B₁₁₁, sample imageB₁₁₂, and sample image B₁₁₃ in addition to the sample image B₁₁. Asabove described, the sample image B₁₁₁, sample image B₁₁₂, and sampleimage B₁₁₃ may have an image processing parameter combination that isdetermined based on the sample image B₁₁ and the learning of theparameter control unit 110.

Alternatively, as shown in FIG. 20, even when the user changes thepreviously set parameters, the process of setting parameters may beexecuted from the beginning again. Accordingly, when the user selectsthe button 10, a series of processes of displaying sample image A,sample image B, sample image C, and sample image D again, receiving aselection from a user, receiving a confirmation whether the user issatisfied, and setting parameters or proceeding to a next step may beperformed again. When the sample image displayed on the display unit 141in order to prompt the selection of the user is generated by processingan image stored in the sample image DB, and a result of applying the setimage processing parameters to a main image has a difference with asample image, in an example shown in FIG. 20, it is possible to moreaccurately reflect a user's preference by starting anew from an initialstep of the parameter setting process.

Alternatively, the parameters may not be changed by displaying thesample images and selecting one of the sample images by the user asshown in an example of FIGS. 19 and 20, but instead by the user directlyadjusting the image processing parameter values.

The parameters currently set or changed by a user may be included inlearning data of a learning module. In particular, all image processingparameters that are set or changed by the user may be stored in aparameter DB of the storage unit 130, and the learning module of theparameter control unit 110 may learn data stored in the parameter DB.Accordingly, a learning result may be updated whenever the imageprocessing parameter is set or changed, and thus an algorithm of thedetermination module or a structure of an artificial neural network maybe changed.

In the above-described exemplary embodiment, the user is allowed toselect image processing parameters via several steps, and the learningof the parameter control unit 110 is applied before the next step,thereby gradually converging into an optimized parameter that ispreferred by the user. In another example of the image processingapparatus 100, the image processing parameters are set once in aninitial step and then a learning result is applied whenever theparameters are changed by the user, thereby converging into optimizedimage processing parameters. This will be described with reference toFIG. 21.

FIG. 21 is a diagram illustrating an example of setting an imageprocessing parameter once in an initial step and then applying learningwhenever the parameter is changed by a user.

Referring to FIG. 21, when the plurality of sample images 13 a, 13 b, 13c, and 13 d are displayed on the display unit 141, the user may selectthe most preferred sample image from among the displayed images and setinitial image processing parameters based on the selected sample image.Parameters applied to the selected sample image may be set withoutchange, or may be optimized, based on learning of the parameter controlunit 110.

The setting of the initial image processing parameters may beaccomplished upon first powering-up of the image processing apparatus100 and/or periodically. Description of the displayed sample images isthe same as described above.

Even when the initial image processing parameters are set, the user maychange the parameters while using the image processing apparatus 100,and the changed parameters may be stored in the parameter DB. When theparameters are changed, the parameter control unit 110 may apply thelearning to the changed parameters in order to optimize the imageprocessing parameters, and may apply the optimized image processingparameters when the next image is displayed. Accordingly, as thefrequency of use increases, the image processing parameter may begradually optimized appropriately to the user's preference.

The learning module of the parameter control unit 110 may include datastored in the parameter DB in the learning data, and may perform thelearning whenever a new parameter is stored in the parameter DB.Accordingly, the learning result may be updated whenever the parametersare changed, and the learning data is accumulated as the frequency ofuse increases, thus gradually optimizing the image processing parametersappropriately to the user's preference.

FIGS. 22 and 23 are diagrams illustrating another example of providing aplurality of sample images to a user.

In the above-described example, a plurality of sample images aresimultaneously displayed without overlapping each other. However, anexemplary embodiment of the image processing apparatus 100 is notlimited thereto. As described in FIG. 22, the plurality of sample images13 a, 13 b, 13 c, and 13 d may be arranged in front and rear within thepop-up window 13, and rear images may be partially covered by frontimages. In this case, when the user selects a specific sample image, theselected sample image may be moved to the forefront and be shown withoutcovered parts. When the input unit 142 is implemented as a touch panel,the user may select a desired sample image by touching and then swipingor dragging the sample image to the front side. In addition, when theinput unit 142 is implemented as a mouse or trackball, the user mayselect a desired sample image by simply clicking the sample image.

Alternatively, as shown in FIG. 23, the plurality of sample images 13 a,13 b, 13 c, and 13 d may be displayed at the same position but atdifferent times. This is referred to as toggling. For example, when theimages are switched at certain intervals, a first image 13 a isdisplayed and then switched to a second image 13 b after time t. Thesecond image 13 b is switched to a third image 13 c after time t, andsubsequently the third image 13 c is switched to a fourth image 13 dafter time t. In this case, since the images are switched at the sameposition, differences between the images may be maximally displayed.

FIG. 24 is a diagram illustrating an example of enlarging and displayinga portion of a sample image.

As shown in FIG. 24, the plurality of sample images 13 a, 13 b, 13 c,and 13 d are displayed on the display unit 141, and when a user selectsa part of the displayed sample images, the selected part may be enlargedand displayed. The selection of a part of the displayed sample imagesmay be accomplished via a touch or click of a corresponding regionaccording to the type of the input unit 142. For example, when the userhas already known a part which is deemed to correspond to a lesion of apatient, the user may select and enlarge a corresponding position in asample image, and may determine which image processing parameters areappropriate to be applied in order for the user to see the enlargedimage to check the lesion.

FIG. 25 is a diagram illustrating an example of setting an imageprocessing parameter for each human part.

The image processing parameters may be set for each respective part ofthe human body when the image processing apparatus 100 is included inthe medical imaging apparatus 200 and the medical imaging apparatus 200scans several parts of a human body, like general radiography, or whenthe image processing apparatus 100 is included in the central server 300or the user computer 400 and processes images acquired by severalmedical imaging apparatuses.

A respective tissue characteristic may be different for each respectivehuman part, and an image characteristic may be different when the tissuecharacteristic is different. For example, some tissues may be associatedwith high noise in a medical image, and some tissues may be associatedwith low contrast in the medical image. Accordingly, the tissuesassociated with high noise may be focused on decreasing the noise in thecorresponding image, and the tissues associated with low contrast may befocused on increasing the contrast in the corresponding image.

As shown in FIG. 25, a pop-up window 14 including a feature 14 a of ahuman body may be displayed on the display unit 141 before the imageprocessing parameters are set such that a part for which the parametersare intended to be set may be selected. A user may perform the selectionby touching or clicking the part for which the parameters are intendedto be set in the displayed feature 14 a of the human body. As such, whenthe image processing parameters are set and managed independently foreach human part, image processing may be performed optimally forcharacteristics of the scan target as well as the user's preference.

In addition, since the image processing apparatus 100 may be shared by aplurality of users, the image processing parameters may be set andmanaged for each user. For example, when the image processing apparatus100 is included in the medical imaging apparatus 200 or the centralserver 300, a plurality of users share the image processing apparatus100. An account may be allocated to each user, and the user may log into the allocated account and access information which relates to imageprocessing parameters of the user when using the image processingapparatus 100. The setting and changing of the image processingparameters that are performed after the log-in to the account may beapplied only to the corresponding user. In addition, the user may alsolog in to the account using the user computer 400 to share the imageprocessing parameters that are set and managed by the image processingapparatus 100. However, an exemplary embodiment of the image processingapparatus 100 is not limited thereto, and the image processingparameters may be set and managed for each user by using any of variousother methods.

An exemplary embodiment of an image processing method will be describedbelow.

The image processing apparatus 100 according to the above-describedexemplary embodiment may be applied to the image processing methodaccording to an exemplary embodiment. Accordingly, drawings anddescriptions of the above-described image processing apparatus 100 mayalso be applied to the image processing method.

FIG. 26 is a flowchart illustrating an image processing method,according to an exemplary embodiment.

Referring to FIG. 26, in operations 611 and 612, different imageprocessing operations are performed in order to generate and display Nsample images (N is an integer equal to or greater than 2) on thedisplay unit. However, the types or number N of sample images may not belimited. The types of the sample images may be determined according toor irrespective of the type of medical image processed by the imageprocessing apparatus 100. In addition, the number of sample images maybe set or changed by a user. Image processing may be performed on aplurality of sample images according to a parameter combinationgenerally preferred by many users. All or only some of the imageprocessing parameters may be set to different values. Whether the imageprocessing parameters applied to the respective sample images are variedis not limited. However, a combination of the image processingparameters applied to one sample image may vary. The plurality of sampleimages may be displayed as shown in FIG. 13, 22, 23, or 24. However,these sample images are merely exemplary and may be displayed in othermethods.

In operation 613, a selection of one from among the sample images isreceived from a user. The selection may be accomplished by touching orclicking a desired image from among displayed sample images according tothe type of the input unit 142.

When the user is satisfied with the selected sample image (i.e., yes inoperation 614), an image processing parameter is set based on theselected sample image in operation 617. An optimal image processingparameter combination preferred by a user may be output by inputting acombination of image processing parameters applied to the finallyselected sample image to an algorithm or an artificial neural networkformed by learning, and the image processing parameters may be set tothe output image processing parameter combination. Alternatively, valuesof the image processing parameters applied to the finally selectedsample image may be set without applying the learning.

When the user is not satisfied with the selected sample image (i.e., noin operation 614), new M sample images (M is an integer equal to orgreater than 2) are generated (in operation 615) and displayed (inoperation 616) based on image processing parameters applied to theselected sample image and a prestored learning result with respect to auser's preference. Here, M may be equal to or different from N. Aprocess of receiving a selection of one of sample images from a user andsetting an image processing parameter according to whether the user issatisfied with the selected image or generating and displaying a newsample image is repeated. In this aspect, an image processing parameterto be applied to new image processing may be determined based onlearning of the parameter control unit 110 and the sample image selectedby the user. For example, when the image processing parameters appliedto the sample image selected in the previous step are input to analgorithm or an artificial neutral network formed by the learning of theparameter control unit 110, an optimized image processing parametercombination may be output appropriately based on a user's preference.The image processing unit 120 may perform image processing according tothe output image processing parameter combination. As an example, if thesample image selected in the previous step is an image which has highcontrast, the image processing unit 120 may minimize a variation in thecontrast to keep the high contrast and change other image processingparameters in order to generate a plurality of new sample images.

FIG. 27 is a flowchart illustrating another example of an imageprocessing method, according to an exemplary embodiment. In the example,a selection of three types of image processing parameters is received.

Referring to FIG. 27, N sample images to which image processingparameter 1 has been variably applied are generated (i.e., operation621) and displayed (i.e., operation 622). Other than the imageprocessing parameter 1, the same parameter values may be applied to theN sample images. In addition, a value optimized to the image processingparameter 1 may be applied to the N sample images. In addition, theimage processing parameter 1 may also reflect a user's preference.

In operation 623, a selection of one of the displayed sample images isreceived from the user. Subsequently, N sample images to which imageprocessing parameter 2 has been variably applied are generated (i.e.,operation 624) and displayed (i.e., operation 625). The parametershaving the same value, other than the image processing parameter 2, maybe applied to the sample images. Alternatively, a value optimized to theimage processing parameter 2 may be applied to the sample images. Inaddition, the image processing parameter 2 may also reflect the user'spreference.

In operation 626, a selection of one of the displayed sample images isreceived from the user. Subsequently, N sample images to which imageprocessing parameter 3 has been differently applied are generated (i.e.,operation 627) and displayed (i.e., operation 628). The parametershaving the same value, other than the image processing parameter 3, maybe applied to the sample images. Alternatively, a value optimized to theimage processing parameter 3 may be applied to the sample images. Inaddition, the image processing parameter 3 may also reflect the user'spreference.

In operation 629, a selection of one of the displayed sample images isreceived from the user.

When all selections based on the sequential variations of imageprocessing parameter 1, image processing parameter 2, and imageprocessing parameter 3 are completed, a final sample image to which theimage processing parameters applied to the selected sample images havebeen reflected is generated (i.e., operation 630) and displayed (i.e.,operation 631). This is intended to prompt a confirmation of whether theuser is satisfied (i.e., operation 632), and the image processingparameter 1 applied to the sample image selected in operation 623, theimage processing parameter 2 applied to the sample image selected inoperation 626, and the image processing parameter 3 applied to thesample image selected in operation 629 may be applied to a final sampleimage. Alternatively, the image processing parameters applied to theselected sample image may not be applied without change and may befurther optimized based on learning of the parameter control unit 110.

When the user is satisfied with the final sample image (i.e., yes inoperation 632), the image processing parameter applied to the finalsample image is set without change in operation 633. When the user isnot satisfied (i.e., no in operation 632), the sequential display andselection of the sample image in operations 621-631 are repeated again.

In an example of FIG. 27, the number of types of the image processingparameters is three. However, this is merely an example, and a smalleror larger number of types of image processing parameters may be set.

In the example of FIG. 27, a selection by the user in the previous stepis not reflected in the sample image displayed in the current step.However, according to another example, the selection of the user in theprevious step may be reflected in the sample image displayed in thecurrent step. In particular, when the user selects one sample image fromamong sample images for the image processing parameter 1, the imageprocessing parameter 1 applied to the sample image selected in theprevious step may be applied to the sample image generated in the nextstep. In this case, even when the final sample image is not generatedand displayed separately, a sample image selected in the last step maybe the final sample image.

FIG. 28 is a flowchart illustrating still another example of an imageprocessing method, according to an exemplary embodiment.

Referring to FIG. 28, image processing is variably performed in order togenerate N sample images in operation 641, and the generated N sampleimages are displayed in operation 642. Here, the N sample images may beobtained by performing the image processing operations according to aparameter combination generally preferred by many users.

In operation 643, a selection of one from among N sample images isreceived from a user, and in operation 644, an image processingparameter is set based on the selected sample image. Parameters appliedto the selected sample image may be set without change or may beoptimized based on learning of the parameter control unit 110. Inparticular, the set image processing parameters are initial imageprocessing parameters, and the setting of the initial image processingparameters may be accomplished upon first powering-up of the imageprocessing apparatus 100 and/or periodically.

Even when the initial image processing parameters are set, the user maychange the parameters while using the image processing apparatus 100 inoperation 645, and the changed image processing parameters may be storedin the parameter DB and optimized based on the learning of the parametercontrol unit 110 in operation 646. In operation 647, the parametercontrol unit 110 sets an optimized parameter, that is, an imageprocessing parameter according to a learning result. Accordingly, when anew image is displayed after the change of the parameters, the optimizedimage processing parameter may be applied. Accordingly, as the frequencyof use increases, the image processing parameter may be graduallyoptimized appropriately to the user's preference.

The learning module of the parameter control unit 110 may include datastored in the parameter DB in the learning data, and may perform thelearning whenever a new parameter is stored in the parameter DB.Accordingly, the learning result may be updated whenever the parametersare changed, and the learning data is accumulated as the frequency ofuse increases, thereby gradually optimizing the image processingparameters appropriately to the user's preference.

With the image processing apparatus, the medical imaging apparatus, andthe image processing method according to an exemplary embodiment, it ispossible to intuitively and easily set an image processing parameterwhich is usable for processing a medical image to a user-preferredoptimal value.

Although a few exemplary embodiments have been shown and described, itwill be appreciated by those of skill in the art that changes may bemade in these exemplary embodiments without departing from theprinciples and spirit of the present inventive concept, the scope ofwhich is defined in the claims and their equivalents.

What is claimed is:
 1. An image processing apparatus, comprising: adisplay configured to display a first plurality of sample images; aninput device configured to receive, from a user, a selection of at leastone sample image from among the first plurality of sample images; and aprocessor configured to generate a second plurality of sample images inwhich at least one image processing parameter from among a plurality ofimage processing parameters to be applied to the selected at least onesample image is changed, and to control the plurality of imageprocessing parameters and to change the at least one image processingparameter to be applied to the selected at least one sample image basedon machine learning that is implemented by using an algorithm thatcorresponds to an artificial neural network, wherein, when a parameterchange request is received after the at least one image processingparameter is set, the processor is further configured to generate aplurality of new sample images to which the at least one imageprocessing parameter has been variably applied based on an imageprocessing parameter that is applied to the at least one sample imageselected when the at least one image processing parameter is set.
 2. Theimage processing apparatus of claim 1, wherein the processor is furtherconfigured to apply the changed at least one image processing parameterin order to generate the second plurality of sample images.
 3. The imageprocessing apparatus of claim 1, wherein, the processor is furtherconfigured to repeatedly perform the generation of the second pluralityof sample images, and the display is further configured to repeatedlyperform a display of the generated plurality of second sample images. 4.The image processing apparatus of claim 1, further comprising a storageconfigured to store preference data which relates to respectivepreferences of each of a plurality of users for the plurality of imageprocessing parameters, wherein the preference data includes a respectivesetting history of the plurality of image processing parameters for eachof the plurality of users.
 5. The image processing apparatus of claim 4,wherein the processor comprises: a learning module configured to performmachine learning with respect to the preference data; and adetermination module generated by the learning module.
 6. The imageprocessing apparatus of claim 1, wherein the processor is furtherconfigured to set the at least one image processing parameter based onthe at least one sample image selected by the user.
 7. The imageprocessing apparatus of claim 6, wherein the processor is furtherconfigured to change the at least one image processing parameter to beapplied to the selected at least one sample image and to store thechanged at least one image processing parameter.
 8. The image processingapparatus of claim 1, wherein the processor is further configured toperform at least one image processing operation with respect to amedical image acquired by a medical imaging apparatus connected with theimage processing apparatus in order to generate the second plurality ofsample images.
 9. The image processing apparatus of claim 1, wherein theprocessor is further configured to perform at least one image processingoperation with respect to a medical image stored in a storage in orderto generate the second plurality of sample images.
 10. The imageprocessing apparatus of claim 1, wherein the processor is furtherconfigured to set the at least one image processing parameter for eachpart shown in a medical image with respect to which image processing isto be performed.
 11. The image processing apparatus of claim 1, whereinthe processor is further configured to set the at least one imageprocessing parameter for each user from among a plurality of users. 12.An image processing apparatus, comprising: a display configured todisplay a first plurality of sample images to which a first imageprocessing parameter from among a plurality of image processingparameters has been variably applied to a second plurality of sampleimages to which an nth image processing parameter from among theplurality of image processing parameters has been variably applied,wherein n is an integer that is greater than or equal to two; an inputdevice configured to receive, from a user, a selection of one sampleimage from among the displayed first plurality of sample images; and aprocessor configured to set the first image processing parameter basedon the nth image processing parameter applied to the selected sampleimage and to optimize the plurality of image processing parameters to beapplied to the selected sample image based on machine learning that isimplemented by using an algorithm that corresponds to an artificialneural network, wherein, when a parameter change request is receivedafter the first image processing parameter is set, the processor isfurther configured to generate a plurality of new sample images to whichthe first image processing parameter has been variably applied based onan image processing parameter that is applied to the one sample imageselected when the first image processing parameter is set.
 13. The imageprocessing apparatus of claim 12, wherein the processor is furtherconfigured to store the plurality of image processing parameters thatare applied to the selected sample image.
 14. The image processingapparatus of claim 12, wherein the processor is further configured tostore the optimized plurality of image processing parameters.
 15. Animage processing apparatus, comprising: a display configured to displaya first plurality of sample images to which at least a first imageprocessing parameter from among a plurality of image processingparameters has been variably applied; an input device configured toreceive, from a user, a selection of one sample image from among thedisplayed first plurality of sample images; and a processor configuredto set the at least first image processing parameter based on a secondimage processing parameter from among the plurality of image processingparameters that is applied to the selected sample image, and when the atleast first image processing parameter is changed, to apply machinelearning that is implemented by using an algorithm that corresponds toan artificial neural network to the changed at least first imageprocessing parameter in order to determine a new image processingparameter from among the plurality of image processing parameters,wherein, when a parameter change request is received after the at leastfirst image processing parameter is set, the processor is furtherconfigured to generate a plurality of new sample images to which the atleast first image processing parameter has been variably applied basedon an image processing parameter that is applied to the one sample imageselected when the at least first image processing parameter is set. 16.The image processing apparatus of claim 15, wherein the processor isfurther configured to perform a setting of the at least first imageprocessing parameter upon an initial execution performed by the imageprocessing apparatus or periodically.
 17. The image processing apparatusof claim 16, wherein the processor is further configured to include thechanged at least first image processing parameter in learning data whichis used for the application of the machine learning.
 18. A medicalimaging apparatus comprising: a scanner configured to scan a targetobject in order to acquire a medical image; and an image processingapparatus configured to set at least a first image processing parameterfrom among a plurality of image processing parameters to be applied tothe medical image, wherein the image processing apparatus includes: adisplay configured to display a first plurality of sample images towhich the at least first image processing parameter has been variablyapplied; an input device configured to receive, from a user, a selectionof at least one sample image from among the displayed first plurality ofsample images; a processor configured to generate a second plurality ofsample images for which the at least first image processing parameterapplied to the selected sample image is changed, and to control theplurality of image processing parameters and to change the at least oneimage processing parameter to be applied to the selected at least onesample image based on machine learning that is implemented by using analgorithm that corresponds to an artificial neural network, wherein,when a parameter change request is received after the at least one imageprocessing parameter is set, the processor is further configured togenerate a plurality of new sample images to which the at least oneimage processing parameter has been variably applied based on an imageprocessing parameter that is applied to the at least one sample imageselected when the at least one image processing parameter is set. 19.The medical imaging apparatus of claim 18, wherein the processor isfurther configured to apply the changed at least first image processingparameter in order to generate the second plurality of sample images.20. The medical imaging apparatus of claim 18, wherein, the processor isfurther configured to repeatedly perform the generation of the secondplurality of sample images, and the display is further configured torepeatedly perform a display of the generated second plurality of sampleimages.
 21. An image processing method, comprising: displaying a firstplurality of sample images; receiving, from a user, a selection of onesample image from among the displayed first plurality of sample images;and generating a second plurality of sample images in which at least oneimage processing parameter from among a plurality of image processingparameters to be applied to the selected sample image is changed; andcontrolling the plurality of image processing parameters and changingthe at least one image processing parameter to be applied to theselected sample image based on machine learning that is implemented byusing an algorithm that corresponds to an artificial neural network,wherein, when a parameter change request is received after the at leastone image processing parameter is changed, the processor is furtherconfigured to generate a plurality of new sample images to which the atleast one image processing parameter has been variably applied based onan image processing parameter that is applied to the one sample imageselected when the at least one image processing parameter is changed.22. The image processing method of claim 21, further comprising settingthe at least one image processing parameter based on the selected sampleimage.
 23. The image processing method of claim 22, wherein the settingthe at least one image processing parameter comprises changing the atleast one image processing parameter to be applied to the selectedsample image and storing the changed at least one image processingparameter.
 24. An image processing method, comprising: obtaining a firstimage generated by a medical imaging apparatus; using the obtained firstimage to generate a first plurality of sample images by varying a firstimage processing parameter from among a plurality of image processingparameters with respect to the obtained first image; receiving, from auser, a first selection of an image from among the generated firstplurality of sample images; setting the first image processing parameterbased on the first user-selected image and based on machine learningthat is implemented by using an algorithm that corresponds to anartificial neural network; using the first user-selected image togenerate a second plurality of sample images by varying a second imageprocessing parameter from among the plurality of image processingparameters with respect to the first user-selected image; receiving,from the user, a second selection of an image from among the generatedsecond plurality of sample images; and setting the second imageprocessing parameter based on the second user-selected image and basedon the machine learning that is implemented by using the algorithm thatcorresponds to the artificial neural network, wherein using the obtainedfirst image to generate the first plurality of sample images comprisescontrolling all of the plurality of image processing parameters otherthan the first image processing parameter to be constant, and whereinthe using the first user-selected image to generate the second pluralityof sample images comprises controlling all of the plurality of imageprocessing parameters other than the second first image processingparameter to be constant.
 25. The image processing method of claim 24,further comprising: using the second user-selected image to generate athird plurality of sample images by varying a third image processingparameter with respect to the second user-selected image; receiving,from the user, a third selection of an image from among the generatedthird plurality of sample images; and setting the third image processingparameter based on the third user-selected image.
 26. The imageprocessing method of claim 24, wherein each of the first imageprocessing parameter and the second image processing parameter includesat least one from among a noise parameter, a sharpness parameter, and acontrast parameter.
 27. The image processing method of claim 24, whereinthe medical imaging apparatus includes at least one from among a medicalultrasound imaging apparatus, an X-ray radiography apparatus, a magneticresonance imaging (MRI) apparatus, a mammography apparatus, a positronemission tomography (PET) apparatus, a computed tomography (CT)apparatus, a single photon emission computed tomography (SPECT)apparatus, and an optical coherence tomography (OCT) apparatus.